Constructing HairDB to facilitate exposome research using human hair
Bibliographic record
Abstract
This study introduces HairDB, an online database serving as a comprehensive repository of hair-related chemicals for exposome research. HairDB was created via an integrative approach. It first extracted 4,184 unique hair-related chemicals through text mining of over 34 million PubMed abstracts and 5.2 million PubMed Central articles, followed by manual data checking. HairDB also applied an artificial intelligence-enabled search to discover organic aerosol biomarkers in literature. A set of 768 chemicals used in hair-related products was then curated through a combination of manual searches and data extraction from the Cosmetic Ingredient Database (CosIng) of the European Union. From manually reading review papers, 29 organic aerosol biomarkers were extracted. Furthermore, 3,679 known exposure chemicals extracted from the Toxin and Toxin Target Database (T3DB) were incorporated in HairDB to represent the possible environmental exposures detected on hair surfaces. The comprehensive set of chemicals captured in HairDB represents the current knowledge of what can be found in and on hair. HairDB was constructed as a user-friendly web interface, allowing easy exploration of hair-related chemicals and tailored for annotating mass spectrometry-based hair exposomics data. The development of HairDB marks an important step forward in using hair as a biological matrix for chemical exposure measurement, facilitating the adoption of hair for exposome research. HairDB is publicly available at https://www.hairdb.ca/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".